Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease.

Background: In patients with suspected obstructive coronary artery disease (CAD), evaluation using a pre-test probability model is the key element for diagnosis; however, its accuracy is controversial. This study aimed to develop machine learning (ML) models using clinically relevant biomarkers to predict the presence of stable obstructive CAD and to compare ML models with an established pre-test probability of CAD models.

Methods: Eight machine learning models for prediction of obstructive CAD were trained on a cohort of 1,312 patients [randomly split into the training (80%) and internal validation sets (20%)].

Preparation, characterization, and application of TiO-patterned polyimide film as a photocatalyst for oxidation of organic contaminants.

Photocatalytically active TiO-patterned polyimide (PI) films (PI-TiO) were fabricated using thermal transfer patterning (TTP). When subjected to hot pressing, the TiO nanoparticles electrosprayed on steel mesh templates were successfully transferred and formed checker plate patterns on PI film. FE-SEM studies confirmed that pressing at 350°C and 100MPa was optimum for obtaining patterns with uniform TiO coverage.

Complete atrioventricular block secondary to bortezomib use in multiple myeloma.

Bortezomib is an inhibitor of 26S proteasome, which is an effective treatment for multiple myeloma. The common adverse effects of bortezomib are asthenic conditions, gastrointestinal disturbances, and peripheral neuropathy. Here we describe a patient with dyspnea and general weakness because of complete atrioventricular block while receiving bortezomib.